Ricardo Pimenta

Monitoring external load is critical for informed decision-making processes across sports. In football, external load is routinely monitored during training sessions and competitive matches. Matches are typically given particular emphasis, as they are generally associated with the highest external load and the most pronounced fatigue responses within a microcycle. Consequently, coaches frequently use match-derived external load values as reference points for prescribing training loads throughout this period. However, despite the apparent specificity of this approach and the unique characteristics of match external load, recent research suggests that coaching staffs may be inadequately quantifying the actual external load produced by players during matches, as well as the associated fatigue responses. This limitation is largely attributable to the substantial match-to-match variability observed in external load outcomes, which may lead to misinterpretation of the true load produced by players. Therefore, a normalized approach to external load monitoring, whereby locomotor external loads are expressed to each player’s highest observed load, has been recommended as a more player-specific method for quantifying and interpreting external load in football. This opinion article highlights how the use of match load as a reference for training prescription may lead to inaccurate estimations of the external load imposed on football players. Furthermore, it outlines the potential consequences of match-load-based prescription for microcycle load management, which may ultimately result in suboptimal or ineffective training stimuli in the long term. Finally, we propose a conceptual framework in which external locomotor load metrics are expressed to players’ characteristic- and/or exposure-based reference values. This framework is intended to support a more player-specific interpretation of external load but requires empirical validation before being used as a prescriptive decision-making model.

There is inconsistency in quantifying, defining and interpreting running speed nomenclature in soccer. In particular, the terms “relative”, “individualized”, and “normalized” are frequently used interchangeably, despite referring to different conceptual and methodological approaches. This lack of clarity may affect the interpretation of running intensity and limit comparability across studies. Therefore, the aim of this paper is to clarify the conceptual distinctions between these terms in the context of soccer performance analysis. We distinguish between absolute thresholds (e.g., a value expressed without context), relative comparisons based on external references, individualized reference values (e.g., a value obtained during treadmill protocol), and normalized metrics that express running speed as a proportion of an individual's physiological characteristic (e.g., percentage of the maximum speed). Using theoretical arguments and applied examples, it was shown that many approaches described as “relative” or “individualized” correspond, in methodological terms, to normalization. Moreover, it was also highlight that the interpretation of normalized intensity depends on the reference used, as different tests (e.g., linear sprint tests vs. intermittent field tests) reflect distinct physiological and mechanical characteristics. Clarifying these distinctions is important to improve methodological rigor and consistency in the analysis of running demands. By contextualizing running demands according to each athlete's maximum speed registered until the point of monitoring, this approach may better approximate the normalized locomotor stimulus imposed by a given training load, in relation to the player's true, albeit inherently unknown, maximal potential exposure. Future research should adopt more precise terminology and clearly report the reference values and scaling procedures used when quantifying running demands in soccer.

This study aimed to quantify and compare the absolute daily and weekly external loads and training-to-match ratios of high-speed running distance (HSRD) and sprint distance (SPD) across the microcycle in semi-professional male soccer players. Twenty-four players from a Portuguese fourth-division team were monitored across nine standardized microcycles (7 days; 1 match) during the 2024–2025 competitive season using Global Navigation Satellite System devices. Absolute HSRD and SPD values were calculated for each training day and match, and daily and weekly training-to-match ratios were derived. Linear mixed-effects models assessed the effects of weekday, playing position, and their interaction. Match day elicited the highest absolute HSRD and SPD across all positions (p < 0.001). Within the training week, HSRD peaked on MD-3, whereas SPD peaked on MD-2, with the lowest values observed on MD-1. Significant position-by-day interactions were identified for both HSRD and SPD (p < 0.001). Wide defenders and midfielders accumulated greater absolute HSRD during both training and matches, while central defenders exhibited comparatively higher training-to-match ratios despite lower absolute match demands. Notably, in the analyzed team, all playing positions exceeded 100% of match-derived HSRD and SPD during the training week. In conclusion, the semi-professional soccer players investigated herein accumulated weekly high-speed and sprinting loads that exceeded match demands, although their distribution varied by playing position and training day. Confirmatory studies are necessary to further explore training-to-match load ratios across different competitive levels.

This study compared physical and technical performance parameters of matches across three consecutive seasons (2020–2023) in the Turkish Super League (TSL). A total of 936 official matches were analyzed. Four physical and fourteen technical performance parameters were evaluated using one-way analysis of variance (ANOVA) applied to season-averaged match data. Performance parameters demonstrated overall improvements across the three seasons; total distance covered (TD) ∼6% corresponding to a large effect size (ES) (p < .001, η ² = 0.20), high-intensity running (HSR) ∼10% reflecting a medium ES (p < .001, η ² = 0.12), sprint ∼20.3%; however, this change was associated with a small-to-medium ES (p < .001, η ² = 0.10), maximal speed (MSS) ∼1.2% corresponded to a small ES (p < .001, η ² = 0.06). Offensive performance showed gains in expected goals (xG) ∼13%, box entries ∼7.2%, passes ∼3%, completed passes ∼2.6%, key passes ∼20% and completed key passes ∼17.5% (all p < 0.05). Defensive actions also improved, with interceptions ∼5% and duels ∼4%, though the magnitude of change corresponded to a small effect size (η ² ≈ 0.01). These findings suggest progressive enhanced physical and technical performance, indicating an overall increase in match intensity across seasons in the TSL.

Sleep is essential for adequate physical recovery and performance, but adolescent athletes face unique challenges that can disrupt sleep. Thus, this observational study aimed to monitor sleep in highly trained male adolescent soccer players between three distinct periods: (1) concurrent school and training; (2) morning training only (during the school winter break); and (3) a full holiday period (with no school or training) and assess the influence of morning alarm use on the differences between periods. Thirty-four male academy soccer players (16.6 ± 0.9 years) were monitored for five weeks using wrist-worn actigraphy and daily sleep diaries. Sleep outcomes included time in bed (TIB), total sleep time (TST), sleep timing, and the Sleep Regularity Index (SRI). Linear mixed-effects models were used to compare sleep variables across periods and to test interactions with morning alarm use. Mean sleep duration across the monitoring period was 06:47 ± 01:10 h, with none of the participants achieving the recommended minimum of 8 h of daily sleep. TIB and TST were slightly lower during the training-only period compared with the other periods (− 18 to − 31 min; trivial to small effects). Holidays were characterized by substantially later bedtimes (~ 1.5–2 h; moderate effects) and wake-up times (~ 2 h; moderate effects), without a meaningful increase in sleep duration (3 to 21 min; trivial effects). Marked differences in SRI were observed between periods (moderate to large effects), with higher SRI during structured periods and the lowest regularity during holidays (moderate to large effects). Not using an alarm resulted in longer TIB (+ 87 min; large effects) and TST (+ 66 min; moderate to large effects), particularly during holidays. The main results highlight that highly trained male adolescent soccer players had later sleep timing during holidays, which did not necessarily promote longer sleep; instead, it was associated with reduced sleep regularity. Moreover, morning alarm use negatively influenced sleep opportunity.

Load monitoring is an essential process for optimizing player performance, requiring accurate quantification of external training load. In soccer, locomotor-related external load is typically analyzed considering high-intensity actions such as sprints, high-speed running, and accelerations (ACC) and decelerations (DEC). Indeed, ACC and DEC represent a significant portion of the total external load produced by a soccer player during a game and vary considerably depending on factors such as playing position, competitive level, age category, and game-to-game variability. Furthermore, given the associated metabolic cost and increased eccentric component, these actions contribute to the development of high fatigue levels, as evidenced by increased biochemical and neuromuscular fatigue markers. However, several studies report ACC and DEC outputs using arbitrary absolute thresholds (AATs) to characterize ACC and DEC efforts, thereby neglecting the relative capacities of each soccer player. Consequently, quantifying ACC and DEC volumes using AATs may under- or overestimate individual soccer player performance by failing to consider their maximum ACC and DEC. Because inaccurate load monitoring may lead to underperformance and increased likelihood of injury occurrence, this review explores the plausible benefits of implementing normalization-based approaches to quantify ACC and DEC outputs, as opposed to relying on AATs.

Objectives: The purpose of this study was to determine whether hamstrings’ passive and active shear moduli measured before and after a fatigue task are correlated. Studying the correlation between passive and active shear moduli is important because, if correlated, passive SWE could provide a quicker assessment without requiring fatigue-inducing voluntary contractions. Methods: Forty-seven football players with no history of hamstring strain injury participated. Muscle shear modulus was assessed only in the dominant lower-limb (dominance defined as the preferred kicking limb) using ultrasound-based shear wave elastography at rest and during isometric contractions at 20% of maximal voluntary isometric effort before and immediately after a 10 × 30 m repeated sprint protocol. Results: Regarding sprint performance, a significant decrease of 8.3% was seen between the first and the last sprints (first: 7.14 ± 0.27 m/s; last: 6.60 ± 0.31 m/s; p < 0.001; dz = 1.88 [1.40–2.35]). In relation to the peak torque normalized to bodyweight, a significant decrease of 9.2% was seen between pre and post (pre: 1.98 ± 0.30 Nm/kg; post: 1.83 ± 0.31 Nm/kg; p < 0.001; dz = 0.89 [0.78–0.95]). Regarding the correlation analysis, none of the passive and active shear moduli measures was significantly correlated in any condition (Bonferroni correction for multiple comparisons, significance threshold set at p < 0.004). Conclusions: The results suggest that the hamstrings’ passive and active shear moduli are not correlated after a fatigue task.

Muscular strength plays a crucial role in sports performance and is often evaluated using vertical jump tests such as the Squat Jump (SJ) and Countermovement Jump (CMJ). Measurements based on flight time (FT) assume that takeoff and landing postures are identical, yet differences in ankle position can introduce systematic errors. This study examined whether dorsiflexion (DF) or plantarflexion (PF) of the ankle during the flight phase affects jump height. Forty-three active university students completed four repetitions each of SJ and CMJ under DF and PF across two sessions. Jump heights were recorded using a Chronojump-Boscosystem platform. No significant difference was observed in SJ between DF and PF, while CMJ heights were consistently higher under DF (DF: 28.29 cm ± 7.7 cm vs. PF: 27.08 cm ± 7.03 cm, p = 0.001; d = 0.16). Notably, the effect of DF appeared more pronounced in CMJ, suggesting that higher jumps are more sensitive to postural variations. These findings could suggest that DF can artificially increase jump heights as measured on a jump platform, without reflecting true improvements in force production. Coaches and practitioners should interpret FT-derived data with caution, particularly for higher jumps. Future research combining precise motion capture with force platforms could directly track center-of-mass changes and validate this mechanism.

Change-of-direction (COD) capacity is a key performance metric in football due to the high volume of COD actions occurring during match play. This study aimed to (i) evaluate the repeatability of the 5-0-5 test and (ii) examine the relationship between acceleration and deceleration profiles of the 5-0-5 test and in-season peak performance. Nineteen national-level Portuguese football players competing in the under-23 Portuguese National Championship were analysed. Repeatability analysis was conducted using intraclass correlation coefficients (ICC). Paired-samples t-tests and Pearson correlations (r) were employed to assess within-subject differences and associations between 5-0-5 measures (highest accelerations from a standing start [ACC_S], deceleration prior to the change in direction (DEC_COD), acceleration after decelerating and changing direction [ACC_COD]) and seasonal peak performances (ACC_max). The in-season peak values were determined from the average of the three maximal values that occurred either in training sessions or in matches. Normalized (relative to seasonal performance) parameters showed good repeatability (ICC = 0.76–0.85). The best ACC_COD and DEC_COD were not significantly different from ACC_max [ACC_COD] vs. ACC_max: (5.04 ± 0.39 m/s² vs. 5.36 ± 0.54 m/s²; p > 0.05) and DEC_max [DEC_COD] vs. DEC_max: (−6.47 ± 0.26 m/s² vs. −6.35 ± 0.61 m/s²; p > 0.05), respectively. The average and best 5-0-5 ACC_S performances showed significant, moderate correlations (r = 0.48; p = 0.040 and r = 0.50; p = 0.028, respectively) with the players’ peak in-season acceleration performance. Therefore, the best ACC_S and ACC_COD attempts can reflect values above 90% of ACC_max, while DEC_COD reflects values above 100% of DEC_max and may assist in monitoring and tailoring training for this capacity on an individual basis.